Optimal cooperation-trap strategies for the iterated Rock-Paper-Scissors game

In an iterated non-cooperative game, if all the players act to maximize their individual accumulated payoff, the system as a whole usually converges to a Nash equilibrium that poorly benefits any player. Here we show that such an undesirable destiny is avoidable in an iterated Rock-Paper-Scissors (RPS) game involving two players X and Y. Player X has the option of proactively adopting a cooperation-trap strategy, which enforces complete cooperation from the rational player Y and leads to a highly beneficial as well as maximally fair situation to both players. That maximal degree of cooperation is achievable in such a competitive system with cyclic dominance of actions may stimulate creative thinking on how to resolve conflicts and enhance cooperation in human societies.Comment: 5 pages including 3 figure

Partition Function Expansion on Region-Graphs and Message-Passing Equations

Disordered and frustrated graphical systems are ubiquitous in physics, biology, and information science. For models on complete graphs or random graphs, deep understanding has been achieved through the mean-field replica and cavity methods. But finite-dimensional `real' systems persist to be very challenging because of the abundance of short loops and strong local correlations. A statistical mechanics theory is constructed in this paper for finite-dimensional models based on the mathematical framework of partition function expansion and the concept of region-graphs. Rigorous expressions for the free energy and grand free energy are derived. Message-passing equations on the region-graph, such as belief-propagation and survey-propagation, are also derived rigorously.Comment: 10 pages including two figures. New theoretical and numerical results added. Will be published by JSTAT as a lette

Spike Pattern Structure Influences Synaptic Efficacy Variability Under STDP and Synaptic Homeostasis. II: Spike Shuffling Methods on LIF Networks

Synapses may undergo variable changes during plasticity because of the variability of spike patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations) influence the efficacy variability under pair-wise additive spike-timing dependent plasticity (STDP) and synaptic homeostasis (the mean strength of plastic synapses into a neuron is bounded), by implementing spike shuffling methods onto spike patterns self-organized by a network of excitatory and inhibitory leaky integrate-and-fire (LIF) neurons. With the increase of the decay time scale of the inhibitory synaptic currents, the LIF network undergoes a transition from asynchronous state to weak synchronous state and then to synchronous bursting state. We first shuffle these spike patterns using a variety of methods, each designed to evidently change a specific pattern statistics; and then investigate the change of efficacy variability of the synapses under STDP and synaptic homeostasis, when the neurons in the network fire according to the spike patterns before and after being treated by a shuffling method. In this way, we can understand how the change of pattern statistics may cause the change of efficacy variability. Our results are consistent with those of our previous study which implements spike-generating models on converging motifs. We also find that burstiness/regularity is important to determine the efficacy variability under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause efficacy variability when the network moves into synchronous bursting states (the states observed in epilepsy)

Optimal memoryless CT strategy.

<p>The optimal values of both players' expected payoff per round and are shown in the upper panel (in units of NE payoff ) for each fixed value of , while the optimal values of the CT strategy's choice probabilities , and are shown in the lower panel. When the NE mixed strategy is better for player X than the CT strategy.</p

The Rock-Paper-Scissors game.

<p>(A) The payoff matrix. Each matrix element is the payoff of the row player X's action in competition with the column player Y's action. (B) The cyclic (non-transitive) dominance relationship among the three candidate actions: Rock () beats Scissors (), beats Paper (), and in turn beats .</p

Optimal CT strategy of unit memory length.

<p>The optimal values of both players' expected payoff per round and are shown in the upper panel (in units of NE payoff ) for each fixed value of , while the optimal values of the CT strategy's choice probabilities , and are shown in the lower panel. When the NE mixed strategy is better for player X than the CT strategy.</p

Optimal CT strategy of finite memory length.

<p>The optimal values of both players' expected payoff per round and are shown in the upper panel (in units of NE payoff ) for each fixed value of , while the minimal memory length of the CT strategy is shown in the lower panel.</p